Parallel and Distributed Systems Laboratory leads the initiative for performing research work mainly for resource management and job/task scheduling aspects in Parallel and Distributed Systems.

This area is further divided into many sub areas such as Grid Computing, Mobile Computing, Cloud Computing and Internet of Things (IoT). Since last few years, group members are working with wider aspects of Mobile Computing, Cloud Computing, Fog Computing and Internet of Things(IoT).

Parallel Systems

This area focuses on Resource Management in Multicore and Manycore systems. Various aspects such as Scheduling, Load Balancing with optimization of various characteristic parameters such as finish time, energy etc. are considered. Many new and effective parameters have been proposed applying heuristics and machine intelligence techniques to solve this problem.

Grid Computing

In the area of Grid Computing, main focus is on Resource allocation and Job scheduling. Many novel and efficient techniques for resource management have been proposed that has resulted in the number of good publications. Work applies some straight techniques and few machine intelligence techniques to solve this problem. The results have been quite encouraging.

Group also explored the area of Security in Computational Grid. Some very novel and good techniques to offer security for security aspiring applications in Grid Computing have been proposed. Many of these techniques even are being explored for its incorporation in the upcoming Grid middleware.

Mobile Computing

In the area of Mobile Computing, focus is on network resource management in mobile computing. A good resource management may not only save lots of energy, but also may result in the efficient scheduling in terms of time and handling a good number of mobile users.

The concept of Mobile Computing can be divided into three parts:

  • Mobile Communication - specifies a framework that is responsible for the working of mobile computing technology
  • Mobile Hardware - consists of mobile devices or device components that can be used to receive or access the service of mobility
  • Mobile Software - a program that runs on mobile hardware

Group members took the problem of Channel Allocation applying various techniques to optimize a few characteristic parameters e.g. Blocking, Reliability etc. Very remarkable results have been obtained resulting in the publications of Research papers in topmost Journals.

Group member also focused on three parameters viz. Bandwidth, Buffer and CPU Time for its optimal allocation. A good number of machine intelligence techniques for optimization of these parameters has been applied. The work has resulted in the publications of good numbers of research papers.

Cloud Computing

Cloud Computing is another upcoming area that deals with many aspects of Cloud resources. This area may lists several research issues such as:

  • Infrastructure Management
  • Platform Management
  • Software Management

Group members are actively working over the problem of Resource provisioning using various different methods. Different types of workflows are being considered for this. Security aware resource provisioning is also being explored by the group member

IoT Internet of Things

Around 2008-09 the term IoT was coined by Kevin Ashton MIT, Auto-ID Centre, 1999. Since then it has proliferated in the globe. It suggest to keep every object on the earth over the Internet. It has become possible with the development of the IPv6 in place of IPv4. Besides, back end technology from Cloud Computing and Big data technology are also have been supportive in the proliferation of IoT. IoT amounts to great deal of research by the researchers/academicians/Scientists of various discipline viz. Computer Science, Electronics, Electrical, Mechanical, Statistics and Mathematics.

Currently, the group members are actively working in the varied areas of IoT such as IoT services, IoT sensor management, IoT applications etc.

Evolutionary Computing

Evolutionary Computing is the area that proposes and applies various techniques that are derived from the nature. In this, the process of natural evolution is used as a role model for a strategy for finding optimal or near optimal solutions for a given problem. In genetic algorithms, an important class of evolutionary computing techniques, candidates for a solution are encoded in a string, often a binary string only containing ‘0’s and ‘1’s. Evolution takes place by modifying the genetic code of a candidate.

Group members are exploring how to improve existing evolutionary techniques as well as proposal of new evolutionary technique.